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Semantic search

Semantic search seeks to improve search accuracy by understanding the searcher's intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results.[1] Major web search engines like Google and Bing incorporate some elements of semantic search. In vertical search, LinkedIn publishes their semantic search approach to job search by recognizing and standardizing entities in both queries and documents, e.g., companies, titles and skills, then constructing various entity-awared features based on the entities.[2]

Guha et al. distinguish two major forms of search: navigational and research.[3] In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. Semantic search is not applicable to navigational searches. In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search.

Rather than using ranking algorithms such as Google's PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results. However, Google itself has subsequently also announced its own Semantic Search project.[4]

Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrand et al. provide an overview that lists semantic search systems and identifies other uses of semantics in the search process.[5]

Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies and XML as found on the Semantic Web.[6] Such technologies enable the formal articulation of domain knowledge at a high level of expressiveness and could enable the user to specify their intent in more detail at query time.[7]

Such processes make use of other information present in a semantic analysis system and takes into account the meanings of other words present in the sentence and in the rest of the text. The determination of every meaning, in substance, influences the disambiguation of the others, until a situation of maximum plausibility and coherence is reached for the sentence. All the fundamental information for the disambiguation process, that is, all the knowledge used by the system, is represented in the form of a semantic network, organized on a conceptual basis.

In a structure of this type, every lexical concept coincides therefore with a semantic network node and is linked to others by specific semantic relationships in a hierarchical and hereditary structure. In this way, each concept is enriched with the characteristics and meaning of the nearby nodes.

Every node of the network (called Synset) groups a set of synonyms which represent the same lexical concept (called Synsets) and can contain:

The attributes of semantic search (those qualities that make it distinct from non-semantic search) are not all necessarily advantages by definition. For example, some attributes may improve search accuracy because of an exhaustive reiterative process but by effect overconsume time and/or resources. Accordingly, these ten attributes are merely salient features although the underlying assumption is that under perfect conditions they are generally preferable.

Handling morphological variations

Handling synonyms with correct senses

Handling generalizations

Handling concept matching

Handling knowledge matching

Handling natural language queries and questions

Ability to point to uninterrupted paragraph and the most relevant sentence

Ability to Customize and Organic Progress

Ability to operate without relying on statistics, user behavior, and other artificial means